Credit risk value based detection of multiple selfish node attacks in cognitive radio networks

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 542
CREDIT RISK VALUE BASED DETECTION OF MULTIPLE SELFISH
NODE ATTACKS IN COGNITIVE RADIO NETWORKS
P.V.Niranchana1
, K. Anish Pon Yamni2
1
PG Student, Department of Electronics and communication engineering, Arunachala College of Engineering for Women,
Vellichanthai-629203, Tamilnadu
2
Assistant Professor, Department of Electronics and communication engineering, Arunachala College of Engineering for
Women, Vellichanthai-629203, Tamilnadu.
Abstract
The scope of the Cognitive Radio technology is to solve the spectrum scarcity problem by allocating the spectrum dynamically to
unlicensed users. In wireless ad-hoc networks, wireless device service degrades the network performance when spectrum is crowded.
Licensed spectrum refers to the portions of the spectrum reserved by each country’s equivalent of the FCC for specific uses. This
portion of the spectrum is further subdivided into frequency bands for military, television signals and commercial services.
Designated users utilize the resource exclusively without interference. In Cognitive radio (CR), free spectrum bands that are not being
used by the licensed users are used by the unlicensed secondary users. Unlicensed secondary users (SU) which transmits the fake
information for its future use Is termed as selfish users. CR networks are vulnerable to selfish attacks, based on spectrum sharing,
spectrum sensing and cognitive capability. SU broadcasts fake information to other neighbouring SUs inorder to occupy all (or) a
part of the available channel. Selfish nodes highly degrade the network performance. In this paper, we have identified the multiple
selfish node attacks using the credit risk information. We propose a method that uses CRV (Credit Risk Value) which helps to detect
more than one selfish node. This improves the network performance of the network.
Keywords: Mobile ad hoc network, Selfish nodes, Cognitive radio, Secondary users, Primary users
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1. INTRODUCTION
Mobile ad-hoc network (MANET) is a infrastructure less
network; hence they are more prone to attacks. Selfish attack
is one of them. In CR technology, primary users also called as
licensed users and secondary users are called as unlicensed
users or cognitive users. The unoccupied frequency band by
the primary users called as spectrum holes or white space. The
fundamental task of CR network is to detect the licensed users.
If they are present, then identify the available spectrum. This
process is called spectrum sensing.
Cognitive radio utilizes the maximum available licensed
bandwidth for unlicensed users. In traditional spectrum
management, most of the spectrum is allocated to licensed
users for exclusive use. CR technology is carried out in two
steps. First, it searches for available spectrum bands by a
spectrum-sensing technology for unlicensed secondary users
(SUs). When the licensed primary user (PU) is not using the
spectrum bands, they are considered available. Second,
available channels will be allocated to unlicensed SUs by
dynamic signal access behavior [12]. Whenever the PU is
present in the CR network, the SU will immediately release
the licensed bands. CR nodes compete to sense available
channels. But some SUs are selfish, and try to occupy all or
part of available channels. Usually selfish CR attacks are
carried out by sending fake signals or fake channel
information. If a SU recognizes the presence of a PU by
sensing the signals of the PU, the SU won‟t use the licensed
channels. In this case, by sending faked PU signals, a selfish
SU prohibits other competing SUs from accessing the
channels.
Because of the dynamic characteristics of CR networks, it is
impossible to use the selfish attack detection techniques used
in traditional wireless communications for CR networks. In
the existing COOPON (Cooperative neighboring cognitive
radio Nodes) mechanism, it is not possible to detect selfish
nodes if there is more than one selfish secondary user.
COOPON uses the autonomous decision capabilities of an ad-
hoc network, based on exchanged channel allocation
information. In this article, we focus on selfish attacks of SUs
toward multiple channel access in cognitive radio ad-hoc
networks. We assume that an individual SU accommodates
multiple channels for the future purpose. For single selfish
node detection, each SU will regularly broadcast the current
multiple channel allocation information to all of its
neighboring SUs [12]. In our proposed method, for multiple
selfish node attack detection, Credit Risk Value is calculated
for each node in the CR network.

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2. SELFISH NODE BEHAVIOUR
Several nodes will be participated in the MANET for data
forwarding and data packets transmission between source and
destination. All the nodes of MANET will perform the routing
function as mandatory. They must forward the traffic which
other nodes sent to it. Among all the nodes some nodes will
behave selfishly, these nodes are called selfish nodes.
MANET‟s are dynamic topologies with constrained
bandwidth.
A) Dynamic topologies Nodes are free to move arbitrarily;
thus the topology of the network may change randomly and
rapidly at unpredictable times in network. Modification of
transmission and reception parameters such as power may also
impact the topology.
B) Bandwidth constrained: variable capacity links Wireless
links will continue to have significantly lower capacity than
their hard-wired counter parts. The relatively low to moderate
link capacities will leads to the congestion rather than the
exception.
C) Power-constrained operations: Some or all the nodes in a
MANET rely on batteries for their energy. Thus, for these
nodes, the most vital design problem may be that of power
conservation. Any node in MANET may act selfishly, which
means, using its limited resource only for its own profit, since
each node in a network has resource constraints, such as
storage and battery limitations. A node would like to enjoy the
profits provided by the resources of other nodes in the
network, but it should not make its own resource accessible to
help others.
Existing exploration on selfish behaviors in a MANET mainly
focus on network concerns. For network problems at MANET,
some selfish nodes may not transmit data to others to conserve
their own battery constraints. Even though network disputes at
MANET are important, replica allocation is also critical, ever
since the vital goal of using a MANET is to provide data
services to users. We address the problem of selfishness in the
context of replica allocation in a MANET. And the solution
selfish replica allocation [9] in MANET is shown in the below
figure (2.1).
Fig 2.1 Example of selfish replica allocation (Excerpt from
[9])
Non cooperative action, such that the node refuses to
cooperate fully in sharing its memory space with other nodes.
According to the figure [2.1], where nodes N1, N2... N6
maintain their memory space of nodes as M1, M2... M6,
respectively, with the specified access frequency information
in Table. To achieve high accessibility, duplication of data
items is minimized in a group.
Table 2.2 Access frequency table (Excerpt from [9])
3. CR NETWORK ARCHITECTURE
This section provides a detailed description of the Cognitive
radio network architecture. According to the architecture,
cognitive radio networks can be classified as Centralized or
Distributed networks. According to operations point of view,
cognitive radio networks can be classified as licensed band
operation and unlicensed band operation. According to Access
type, cognitive radio network can be classified as CR network
access, CR ad-hoc access, and primary network access. In
Centralized Cognitive network, a base station is used to
manage each CR user in the network. The base station

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communicates directly with each user and controls the
medium access and the secondary users in the network. As
shown in Fig. 3.1, the CR users communicate with each other
in an ad-hoc manner. Information is shared directly between
the secondary users who fall within the communication range
otherwise information is shared over multiple hops.
In Licensed band operation, this band is dedicated for the
primary users in the network. It can be used by the unlicensed
user if not occupied by the primary user. CR user must vacate
the licensed band if the primary user reappears then and move
to another vacant spectrum band. Unlicensed band operation:
The unlicensed users have the same right to use the unlicensed
band. There is no need to vacate the spectrum for the licensed
users. Cognitive Radio network architecture is shown
below[5].
.
Fig 3.1 Cognitive Radio Network Architecture
As shown in Figure, the cognitive users can share information
with their base station on the licensed as well as the unlicensed
spectrum band. Primary network access: CR users can also
communicate with the primary base station on the licensed
spectrum band with an adaptive medium access control
protocol.
3.1 Selfish Node Attacks
Selfish attacks are different depending on what and how they
attack inorder to occupy CR spectrum resources. There are
many different selfish attack types.
First type of attack is designed to prohibit a legitimate SU
(LSU) from sensing available spectrum bands by sending
faked PU signals. The selfish SU (SSU) will emulate the
characteristics of PU signals. A legitimate SU who overhears
the faked signals makes a decision that the PU is now active
and so the legitimate SU will give up sensing available
channels. This attack is usually performed when building an
exclusive transmission between one selfish SU and another
selfish SU regardless of the number of channels. There must
be at least two selfish nodes for this type of attack. There must
be at least two selfish nodes for this type of attack. Second
type of attacks are also a selfish SU emulating the
characteristics of signals of a PU, but they are carried out in
dynamic multiple channel access.
In a normal dynamic signal access process, the SUs will
periodically sense the current operating band to know if the
PU is active or not, and if it is, the SUs will immediately
switch to use other available channels. In this attack type, a
continuous fake signal attack on multiple bands in a round-
robin fashion, an attacker can effectively limit legitimate SUs
from identifying and using available spectrum channels.
Another type of attack is called a channel pre-occupation
selfish attack [12].This type of attacks can occur in the
communication environment that is used to broadcast the
current available channel information to neighboring nodes for
transmission.
In the previous existing methods, there considered a
communication environment that broadcasting is carried out
through a common control channel (CCC) which is a channel
dedicated only to exchanging management information. A
selfish SU will broadcast fake free (or available) channel lists
to its neighboring SUs Even though a selfish SU only uses
three channels, it will send a list of all five occupied channels.
The fake information on channel allocation of each nodes in
the network. Thus, a legitimate SU is prohibited from using
the two available channels. Detection of existing selfish
technologies is likely to be uncertain and less reliable, because
they are based on estimated reputation or estimated
characteristics of stochastic signals.
4. EXISTING METHOD
The existing technique is an intuitive approach but reliable due
to using deterministic channel allocation information as well
as the support of cooperative neighboring nodes.
The efficiency is measured by a detection rate as follows;
Detection Rate =
number of detected selfish SUs
number of actual selfish SUs
One SU has a maximum of eight data channels and one
common control channel. The channel data rate is 11Mb/s. In
simulation, one SU can have two to five one-hop neighboring
SUs. The experiment was performed under various selfish SU
densities in a CR network. The article [12] proposed an
efficient selfish cognitive radio attack detection technique,
called COOPON, (cooperative neighboring cognitive radio
nodes). In traditional spectrum management, most of the
spectrum is allocated to licensed users for exclusive use. CR
technology is carried out in the following steps.

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 First, it searches for available spectrum bands by a
spectrum sensing technology for unlicensed secondary
users (SUs).
 Second, allocate the available spectrum dynamically to
unlicensed users.
When the licensed primary user (PU) is not using the spectrum
bands in the network, they are considered available. Secondary
users emulate the characteristics of the primary users by
sending the fake information. Thus, all of the 1-hop
neighboring SUs will make a decision that the target SU is a
selfish attacker. All 1-hop neighboring SUs sum the numbers
of currently used cannels sent by themselves and other
neighboring nodes. In addition, simultaneously all of the
neighboring nodes sum the numbers of currently used cannels
sent by the target node, TNode. Individual neighboring nodes
will compare the summed numbers of the channel used sent by
all neighboring nodes to the summed numbers sent by the
target node to check if the target SU is a selfish attacker. Here,
the broadcasting is carried out through the Common Control
Channel In the existing COOPON mechanism, first it checks
that all the nodes in the network is validated or not. If not, it
checks the nodes one by one using the COOPON method
described below. The below figure shows the selfish attack
detection algorithm (or) mechanism flow chart of COOPON
[12].
Fig.4.1 Selfish attack detection Mechanism
As mentioned above, all currently used channels in the target
node and the neighboring nodes are summed up in two steps;
the Channeltarget_node and the Channelneigboring_node,
based on the channel allocation information. Channeltarget_node
is the sum of the number of currently used channels to each
neighboring node reported by target node and the
Channelneighboring_node is the sum of no. of the currently used
channels to the target node reported by each neighboring
node.Then Channeltarget_node will be compared to
Channelneigboring_node. In Figure 4.2the target node, T-
Node is also a SU, but other 1-hop neighboring SUs, N-Node
1, N Node2, N-Node 3, and N-Node 4, will scan any selfish
attack of the target node. The target SU and all of its 1-hop
neighboring users will exchange the current channel allocation
information list via broadcasting on the dedicated channel. For
this, it uses the common control channel.
Fig 4.2 COOPON detection mechanism (Excerpt from [12])
The COOPON detection mechanism clearly describes the
number of channels used by each node in the network. It is
noticed that T-Node 2 reports that there are two channels
currently in use, while N-Node 3 reports that there are three
currently in use, which creates a discrepancy. N-Node 4 also
receives faked channel allocation information from the target
node.
On the other hand, all other exchanged information pairs, T-
Node/ N-Node 1 and T-Node/N-Node 2, are correct. Thus, all
of the 1-hop neighboring SUs will make a decision that the
target SU is a selfish attacker. According to the above shown
example, the Channeltarget_node is 7 and the
Channelneighboring_node is 5. Because 7 > 5, the target
secondary node is identified as a selfish attacker. The
COOPON mechanism is reliable than the previous detection
methods, because the channel allocation information is more
deterministic than the stochastic signal characteristics.

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5. PROPOSED SYSTEM
The proposed technique is simple to compute. The proposed
algorithm is the CRV algorithm. The CRV technique will
detect the attacks of selfish SUs in the cognitive radio network
by calculating the credit risk value. In economics, credit risk is
the measured risk of loss due to a debtor‟s nonpayment of a
loan. A bank examines the credit risk of an applicant prior to
approving the loan. The measured credit risk of the applicant
indicates if he/she is credit worthy. Similar approach is
considered in our system. CRV technology is carried out in the
following steps. First it calculates the CRV value before
sending any packet, then route the packet, again recalculate
the CRV value. The CRV value is a constant value, which
denotes the energy consumed for the packet transmission.
CRV = No. of packets ∗ Total energy − Remaining energy
Where,
Total energy is the initial energy of the node and Remaining
energy is the energy after data routing.
In the proposed technique, multiple selfish node attacks are
detected using the above simple formula.CRV value is the
number of packets multiplied by the energy used by the node
in the CR network In this method, topology is constructed first
and then CRV value is calculated for all the nodes in the
network. CRV value is a constant value. Data routing is done
after calculating the CRV value for all nodes. Then recalculate
the CRV value. The CRV value i.e., the energy for each node
packet transmission is inbuild as ten, as the energy required
for each and every normal transmission is near to the value
ten. If the CRV value is greater than ten CRV, then it detects
that node as attacker node and then reroute the packet. Again
calculate the CRV value. If the value is less than ten CRV,
then again data routing is done. And the process repeated as
above. And the process is repeated as above until the routing is
finished.
The proposed selfish attack detection CRV algorithm flow
chart is given below in figure5.1. Cognitive users in the
network can share information with each other in ad-hoc
manner on both the licensed and unlicensed spectrum band.
Based on the spectrum sensing technology, we define a
submission period as certain time duration. Each node needs to
generate monitoring messages to report the performance of its
neighbours in each submission period.
By using the above formula, we can get the energy consumed
for each node‟s packet transmission. After the topology
reconstruction, the CRV value is again calculated for each
node by the above formula. Then it checks whether the
calculated value is between 4 and 10 CRV (or) not until it
checks all the nodes in the network. If the value is in between
4 and 10, then the performance is more efficient. Because, for
normal transmission, the credit won‟t be less than four By
using this simple computing procedure, the attacker node in
the cognitive radio network can be detected and so the
network performance can be improved.
Fig 5.1 CRV algorithm
The CRV value for normal transmission ranges from 4 to 10.
By setting this as thresholding value and then calculating the
CRV value will be more efficient. After calculating the CRV
value, data routing is done again if the CRV value is less than
10.The figure5.2 shows that the CRV value for Node 1 is 8,
Node2 is 10 and Node3 is 7.The CRV value for Node4 is 13,
which is greater than 10.This means that Node 4 is an attacker
node. Thus, more than one selfish node in the cognitive radio
network can be detected by using the Credit Risk Value
(CRV) technique. This method is simple to compute and more
efficient than the existing COOPON method. This method is
also a time consuming method, since the formula for
calculating the CRV value is simple.CRV detection
mechanism is given in the below figure.CRV value is
mentioned near in a small circle to each nodes in the network.

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Fig 5.2 CRV detection mechanism
In identifying selfish nodes in the cognitive radio ad-hoc
network, the procedure for handling error is as follows:
 After the node creation, CRV is evaluated for each
node in the network.
 Compare the CRV score with all other nodes. Then the
high CRV value is set as leader.
 Broadcast the leader id to all other nodes. By
monitoring all the nodes, it identifies selfish node.
6. PERFORMANCE ANALYSIS
Graph is an essential part of display a result. The graph shows
the various result comparisons with packets, throughput,
energy efficient, malicious node detection analysis and packet
delivery ratio with respect to the simulation time.In particular,
selfish nodes are determined to be selfish only when all other
nodes in the group agree with the node‟s Selfishness. We first
compare the overall selfishness method with that of COOPON
to demonstrate the effectiveness of our detection method
(CRV).
We expect that the overall selfishness alarm will be reduced in
query processing by detecting selfish nodes effectively with
CRV, since many selfish nodes will be removed from the
replica allocation phase and many reliable nodes will serve
data requests from nodes. It is desirable to observe truly
selfish nodes to evaluate the effectiveness of the detection
method. A data requester cannot tell an expected node‟s
selfishness from network disconnection, i.e., no reply from the
expected node.
The layout of the simulated network of the routing
performance of CRV on comparison with the existing
COOPON method is shown below.
Fig 6.1 Routing performance on average accessibility
The simulation output of the performance level of Intrusion
Detection System (IDS) between CRV and the existing
method is shown below. In the figure, y-axis represents the
number of nodes and the x-axis represents the selfish node
detection. The performance level increases with our proposed
detection mechanism.
Fig 6.2 Performance level of Intrusion Detection System
Finally, we examine the effect of communication range. The
simulation layout of the performance level of selfish node
detection time and the communication range is shown below.
The selfish node detection time of CRV is less when
compared with that of the COOPON method.

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Fig 6.3 Performance level on selfish node detection time
7. CONCLUSIONS
The existing method detects only one selfish node in cognitive
radio network. Because, the COOPON uses the deterministic
channel allocation information. In this paper, we have
identified the multiple selfish node attacks using the credit risk
information. The Proposed CRV algorithm detects more than
one selfish secondary user in the cognitive radio network
using the Credit Risk Value. Our approach is designed for
cognitive radio ad-hoc networks. The CRV algorithm makes
use of ad-hoc network advantages such as energy required for
packet transmission at each node in the network for better
detection reliabilities. The proposed reliable and simple
computing technique can be well fitted for practical use in the
future
8. REFERENCES
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BIOGRAPHIES
P.V. Niranchana gained bachelor‟s degree
in Periyar Maniammai University,
Tanjavur. Joined in the department of
Electronics and Communication
engineering at Arunachala College of
engineering for women, Kanyakumari as a
PG student in 2012
K. Anish Pon Yamini gained Master‟s
degree in Communication System from
SRM Institute of Science and Technology.
She joined in the department of
Electronics and Communication at
Arunachala College of Engineering for
Women, Kanyakumari as a Assistant professor in
2012.Ms.Anish Pon Yamini has more than 05 research papers
to her credit in various International/National Journals,
Conferences. She is actively involved in research activities in
the ad-hoc wireless networks.

Credit risk value based detection of multiple selfish node attacks in cognitive radio networks

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Credit risk value based detection of multiple selfish node attacks in cognitive radio networks